9,101 research outputs found
Metal Oxidation Kinetics and the Transition from Thin to Thick Films
We report an investigation of growth kinetics and transition from thin to
thick films during metal oxidation. In the thin film limit (< 20 nm), Cabrera
and Mott's theory is usually adopted by explicitly considering ionic drift
through the oxide in response to electric fields, where the growth kinetics
follow an inverse logarithmic law. It is generally accepted that Wagner's
theory, involving self-diffusion, is valid only in the limit of thick film
regime and leads to parabolic growth kinetics. Theory presented here unifies
the two models and provides a complete description of oxidation including the
transition from thin to thick film. The range of validity of Cabrera and Mott's
theory and Wagner's theory can be well defined in terms of the Debye-Huckel
screening length. The transition from drift-dominated ionic transport for thin
film to diffusion-dominated transport for thick film is found to strictly
follow the direct logarithmic law that is frequently observed in many
experiments
A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation
A vital aspect in energy storage planning and operation is to accurately
model its operational cost, which mainly comes from the battery cell
degradation. Battery degradation can be viewed as a complex material fatigue
process that based on stress cycles. Rainflow algorithm is a popular way for
cycle identification in material fatigue process, and has been extensively used
in battery degradation assessment. However, the rainflow algorithm does not
have a closed form, which makes the major difficulty to include it in
optimization. In this paper, we prove the rainflow cycle-based cost is convex.
Convexity enables the proposed degradation model to be incorporated in
different battery optimization problems and guarantees the solution quality. We
provide a subgradient algorithm to solve the problem. A case study on PJM
regulation market demonstrates the effectiveness of the proposed degradation
model in maximizing the battery operating profits as well as extending its
lifetime
MDP Optimal Control under Temporal Logic Constraints
In this paper, we develop a method to automatically generate a control policy
for a dynamical system modeled as a Markov Decision Process (MDP). The control
specification is given as a Linear Temporal Logic (LTL) formula over a set of
propositions defined on the states of the MDP. We synthesize a control policy
such that the MDP satisfies the given specification almost surely, if such a
policy exists. In addition, we designate an "optimizing proposition" to be
repeatedly satisfied, and we formulate a novel optimization criterion in terms
of minimizing the expected cost in between satisfactions of this proposition.
We propose a sufficient condition for a policy to be optimal, and develop a
dynamic programming algorithm that synthesizes a policy that is optimal under
some conditions, and sub-optimal otherwise. This problem is motivated by
robotic applications requiring persistent tasks, such as environmental
monitoring or data gathering, to be performed.Comment: Technical report accompanying the CDC2011 submissio
LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees
We present a method to generate a robot control strategy that maximizes the
probability to accomplish a task. The task is given as a Linear Temporal Logic
(LTL) formula over a set of properties that can be satisfied at the regions of
a partitioned environment. We assume that the probabilities with which the
properties are satisfied at the regions are known, and the robot can determine
the truth value of a proposition only at the current region. Motivated by
several results on partitioned-based abstractions, we assume that the motion is
performed on a graph. To account for noisy sensors and actuators, we assume
that a control action enables several transitions with known probabilities. We
show that this problem can be reduced to the problem of generating a control
policy for a Markov Decision Process (MDP) such that the probability of
satisfying an LTL formula over its states is maximized. We provide a complete
solution for the latter problem that builds on existing results from
probabilistic model checking. We include an illustrative case study.Comment: Technical Report accompanying IFAC 201
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Registration involving one or more images containing pathologies is
challenging, as standard image similarity measures and spatial transforms
cannot account for common changes due to pathologies. Low-rank/Sparse (LRS)
decomposition removes pathologies prior to registration; however, LRS is
memory-demanding and slow, which limits its use on larger data sets.
Additionally, LRS blurs normal tissue regions, which may degrade registration
performance. This paper proposes an efficient alternative to LRS: (1) normal
tissue appearance is captured by principal component analysis (PCA) and (2)
blurring is avoided by an integrated model for pathology removal and image
reconstruction. Results on synthetic and BRATS 2015 data demonstrate its
utility.Comment: Accepted as a conference paper for ISBI 201
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